Regional analysis of specific suspended sediment loads in northern Iran using multivariate statistical techniques

A - Papers appearing in refereed journals

Khaleghia, S., Nosrati, K., Kebriyaeizadeh-Kachourestaghi, S. and Collins, A. L. 2024. Regional analysis of specific suspended sediment loads in northern Iran using multivariate statistical techniques. Hydrological Sciences Journal. pp. 1-12. https://doi.org/10.1080/02626667.2024.2402487

AuthorsKhaleghia, S., Nosrati, K., Kebriyaeizadeh-Kachourestaghi, S. and Collins, A. L.
Abstract

Predicting suspended sediment loads in areas without detailed measurements, or with only short-term records, is crucial for the sustainable management of water resources. This study aimed to establish the relationships between specific suspended sediment loads and the characteristics of 23 sub-basins within the Haraz-Neka River basin, in Iran, to create regional models to estimate the sediment loads. To achieve
this, several analytical methods were used, including cluster analysis, principal component analysis, principal component and classification analysis, and general linear modelling. Among these, the principal component analysis regression model was the most effective for estimating suspended sediment loads in the clusters. The principal component and classification analysis revealed that the best predictor was the first principal component, which strongly correlated with the minimum and mean elevation of the sub-basins. The general linear model regression showed the best overall performance for estimating regional suspended sediment loads in the study area.

KeywordsSuspended sediment load; PCA; PCCA; GLM; Sub-basin characteristics
Year of Publication2024
JournalHydrological Sciences Journal
Journal citationpp. 1-12
Digital Object Identifier (DOI)https://doi.org/10.1080/02626667.2024.2402487
Web address (URL)https://www.tandfonline.com/doi/full/10.1080/02626667.2024.2402487?src=
Open accessPublished as non-open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeResilient Farming Futures (WP2): Detecting agroecosystem ‘resilience’ using novel data science methods
Resilient Farming Futures
S2N - Soil to Nutrition - Work package 3 (WP3) - Sustainable intensification - optimisation at multiple scales
Output statusPublished
Publication dates
Online27 Sep 2024
Publication process dates
Accepted15 Aug 2024
PublisherTaylor & Francis
ISSN0262-6667

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